Deep learning the collisional cross sections of the peptide universe from a million experimental values

ABSTRACT: The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To explore the nature and utility of the entire peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation – serial fragmentation (PASEF). The scale and precision (CV <1%) of our data is sufficient to train a recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools library validate the model within a 1.3% median relative error (R > 0.99). Hydrophobicity, position of prolines and histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human) Escherichia Coli Caenorhabditis Elegans Saccharomyces Cerevisiae (baker's Yeast) Drosophila Melanogaster (fruit Fly)

TISSUE(S): Cell Culture

SUBMITTER: Mario Oroshi  

LAB HEAD: Matthias Mann

PROVIDER: PXD019086 | Pride | 2021-01-18

REPOSITORIES: Pride